Introduction to Grey system theory
The Journal of Grey System
The integration and application of fuzzy and grey modeling methods
Fuzzy Sets and Systems
Practical application of fuzzy logic and neural networks to fractured reservoir characterization
Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
A neural network tool for analyzing trends in rainfall
Computers & Geosciences
Visualizing the Function Computed by a Feedforward Neural Network
Neural Computation
Adaptive fuzzy modeling versus artificial neural networks
Environmental Modelling & Software
Generalized Dynamic Fuzzy Neural Network-Based Tracking Control of Robot Manipulators
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Use of neurofuzzy networks to improve wastewater flow-rate forecasting
Environmental Modelling & Software
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R^2 greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R^2 greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.